Credit Card Fraud detectionCredit Card Fraud detectionApr 2024 - May 2024Apr 2024 - May 2024 Associated with RVS Institute of Management Studies & ResearchAssociated with RVS Institute of Management Studies & Research The primary goal of this project is to build and evaluate machine learning models that can accurately identify fraudulent transactions, thereby helping financial institutions minimize losses due to fraudulent activities. During the EDA process, we analyzed the distribution of fraudulent vs. valid transactions, explored the relationship between transaction amount and fraud status, and investigated any patterns or anomalies in the data that could help in building predictive models. Summary: Logistic regression performed poorly with zero precision and recall. Decision tree and random forest models outperformed logistic regression. Random forest achieved the highest precision and accuracy among the three models.

In this internship, they provide me with dataset variables include students marks and their study hours. I used regression analysis to know and prove about the relationship between those two variables. Atlast I found that there is a strong linear statistically significant relationship between them with p value less than 0.05.after this I build a linear regression model to prediction of students marks based on their study hours.

In KPMG virtual internship, I've been assigned as Data analyst to identify highly potential new customers for sprocket central pvt ltd, that marketing and sales team should target in their future time period, from new customer dataset using past customer and sales data.Three datasets were provided for this analysis they are,1. Customer Demographic2. Customer Addresses3. Transactions data in the past 3 monthsTo achieve outcome three tasks were assigned,Task 1 - Data Quality Assessment(Excel)Task 2 - Data Exploration(Python) and presentation ( MS PowerPoint)Task 3 - Data visualization (Power BI, Tableau)From this Analysis I've found that customers with~ Age – 40 - 50~ Gender - Female~ State – NSW~ Job industry *Financial Services. *Manufacturing *Healthare high potential customers